Animal movement is critical for maintenance of ecosystem services and biodiversity. The study of complex movement patterns and of the factors that control such patterns is essential to inform conservation research and environmental management. Technological advances have greatly increased our ability to track, study, and manage animal movements. But analyzing and contextualizing vast amounts of tracking data can present scientific, computational, and technical challenges that require scientists and practitioners to master new skills from a wide range of computational disciplines.
AniMove (www.animove.org), a collective of international researchers with extensive experience in these topics, teaches a two-week intensive training course for studying animal movement. This two-week course focuses on interdisciplinary approaches linking animal movement with environmental factors to address challenging theoretical and applied questions in conservation biology. To achieve this, participants will acquire significant skills in computational ecology, modeling, remote sensing and Geographic Information Systems (GIS).
During Week 1, participants learn new skills through lectures and hands-on exercises in data collection, management, analysis and modeling approaches. During Week 2, participants work in small groups on conservation projects with datasets provided by course participants or instructors. Participants are encouraged to bring their own data and research questions to be addressed by one of the working groups. Throughout, the course only uses open source software (R, GRASS, QGIS) and relies mainly on open-access environmental datasets. Evening lectures by noted conservation scientists, movement ecologists, and remote sensing experts supplement instruction, providing the broader context of animal movement and remote sensing analysis.
Admission to this two-week course requires prior working knowledge of R, including the ability to write and troubleshoot scripts for processing and analyzing data. Participants must be familiar with common R operations such as data frame subsetting, data inspection, package/library structure and handling of error messages. We do not cover/review these skills during the course itself. More advanced techniques such as functions and loops are briefly introduced in the course. Remote sensing, open source GIS, and modeling knowledge are advantageous but not mandatory for participation.